The Prediction Models for the Optimal Timing of Surgical Intervention for Necrotizing Enterocolitis: Nomogram vs. Five Machine Learning Models

Background: Necrotizing enterocolitis (NEC) is one of the most common diseases that pose serious threats to the life of newborns. In clinical practice, NEC is typically treated by surgical intervention, but it is still difficult to identify the timing of surgical intervention for this disease. Therefore, this study was conducted to establish a machine learning (ML) model for identifying the optimal timing of surgical intervention for NEC by comparing logistic regression (LR) models with ML models and to visualize important influencing indicators via a nomogram. Methods: The basic information, clinical manifestations, laboratory examination results, and radiography imaging results of newborns who were diagnosed with NEC in Qilu Hospital of Shandong University from 2011 to 2024 were collected and processed. Besides, some specific indicators were screened using univariate and multivariate LR analysis and ML analysis methods (including the random forest [RF] algorithm, support vector machine [SVM], decision tree [DT], naive Bayes [NB], and k-Nearest Neighbor [KNN]) to construct a clinical model to predict the timing of surgical intervention for NEC. Moreover, a nomogram for predicting the timing of surgical intervention for NEC was constructed based on the independent risk factors selected by the multivariate LR analysis. Finally, the performance of each ML model was evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: A total of four differential indicators related to surgical intervention for NEC were screened by univariate and multivariate LR analyses. The five ML models were evaluated according to these indicators and then compared with a classical LR model. The results demonstrated that the LR model exhibited the best performance. Among the five ML models, the RF model displayed the best overall performance. Additionally, a nomogram was plotted according to the LR analysis results to visualize the scores of important indicators. The results revealed that interloop space widening had the highest score. Conclusion: The indicator evaluation results and the analysis results based on ROC curves, DCA curves, and calibration curves corroborate that the LR model as a classical model achieves the best performance. In addition to the LR model, the RF model displays excellent performance among the five ML models. Therefore, it is expected to use this ML model to identify a more suitable surgical timing for newborns with NEC.